We are seeking a student interested in robotics, specifically in the areas of tactile sensing, impulse-based (non-prehensile) object manipulation, or other unconventional robotic manipulation, probably with vision assist. The ideal result is a working demo leading to an accepted paper. Applicants should be rising college seniors or graduate students in STEM; prior mechatronics, CAD/CAM, machine vision / machine learning, Python, and open-source development experience is very desirable. The position is immediately available with an expected duration of 3-4 months. This internship requires work that can only be done at MERL.

We are seeking a highly motivated intern to conduct original research extending MERL's work on uncertainty estimation in face landmark localization (the LUVLi model) to the domains of 3D faces and video sequences. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision and machine learning with a strong publication record. Experience in deep learning-based face landmark estimation, video tracking, and 3D face modeling is preferred. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected.